class_docstr: LSTrainer supports GCQ (Gradient Communication Quantization) for distributed multi-machine training
  based on transformers.Trainer.
code: def _wrap_model(self, model, training=True, dataloader=None):
      model = super()._wrap_model(model, training, dataloader)
      # Enable GCQ.
      if isinstance(
          model, torch.nn.parallel.DistributedDataParallel
      ) and getattr(self.gcq_args, "enable_GCQ", False):
          assert version.parse(torch.__version__) >= version.parse(
              "1.10"
          ), "Training with GCQ requires that the version of torch has to be greater than or equal to 1.10!"
          state = GCQState(
              process_group=dist.group.WORLD
              if dist.is_initialized()
              else None,
              hidden_size=self.gcq_args.hidden_size,
              quantile_value=self.gcq_args.GCQ_quantile,
          )
          # Register the communication hook.
          model.register_comm_hook(state=state, hook=encode_and_decode)
          logger.info(
              "############ register communication hook done ###########"
          )
  
      return model
  
file_path: lightseq/examples/training/huggingface/gcq/ls_hf_gcq_trainer.py
imports: import logging
  import torch
  import torch.distributed
  from transformers import Trainer
  from packaging import version
  from lightseq.training.gcq import GCQState, encode_and_decode
  from examples.training.huggingface.gcq import GCQArguments
label_desc: Use image node_id 2 for calling the LSTrainer obj's underlying member method code with example usage: obj._wrap_model(model, training, dataloader) and returns: model